AI Learning Hub: Tutorials & Guides

Latest AI Resources

Explore in-depth articles, video explainers, and tutorials from the Vidvatta Learning Hub.

Frontier Models, Broken Boundaries, and Why Sandboxing Is Core AI Infrastructure

Learn what frontier models are, how AI crossed cyber boundaries, and how sandboxing, RAG controls, and containment reduce real deployment risk.

Vidvatta Team03 Aug 2026

Safety failuresLLMRAG

AI Safety Failures in LLM, RAG, and Agent Systems: Long-Horizon Risks, Root Causes, and Practical Mitigations

Research-grade guide to AI safety failures in LLM, RAG, and agents: causes, evaluations, long-horizon risks, mitigations, and practice.

Vidvatta Team26 Jul 2026

AI-AgentsAttention Mechanism

Attention in AI Agents: How Context Drives Memory, Retrieval, and Better Decisions

Learn how attention powers AI agents with contextual memory, RAG, and smarter decisions using practical architectures, tradeoffs, and examples.

Vidvatta Team26 Jul 2026

Multi-AgentsAI

Multi-Agent AI in the Real World: Architectures, Use Cases, and Deployment Tradeoffs

A practical guide to multi-agent AI systems: real use cases, architectures, deployment tradeoffs, common mistakes, and implementation patterns.

Vidvatta Team19 Jul 2026

LLMSystem Design

How a Production LLM System Like Kimi-k3 Probably Serves Billions of Requests

practical guide to LLM production architecture, scaling, RAG, agents, ops tradeoffs, and common mistakes behind systems like Kimi-k3.

Vidvatta Team19 Jul 2026

LangchainGuardrailsBFSI

Guardrails with langchain middleware in a finance

This blog explains how to use LangChain middleware as a guardrail layer for financial document extraction RAG systems. It covers why guardrails matter, how middleware controls retrieval and model behavior, practical validation patterns, audit-ready workflows, common failure modes, testing strategies, and when human review is required.

Vidvatta Team13 Jul 2026

EvalsAI GuardrailsAI Evaluation Metrics

Evals with Real Life Example

testing an LLM application is like grading a student's exam paper. An LLM's response acts as the student's answer sheet, a test case acts as the exam question and expected answer, a metric acts as the marking scheme, and the threshold serves as the minimum passing mark

Vidvatta Team04 Jun 2026

LLMKnowledge GraphMemory

Building LLM memory from scratch

In LLM ,memory is one of the important ascepts of preventing halluciation and prevent contextual leaks with the LLM. This is tackled using memory attention

Vidvatta Team03 Jun 2026

AI Learning Hub Tutorials

Featured AI Tutorials

Build AI Agents from Scratch with Python and Gemini: A Beginner Friendly Guide to Use Cases and Challenges